# A function approximation approach to the prediction of blood glucose levels

arXiv: https://arxiv.org/abs/2105.05893 · HTML: https://mlsanity.com/paper/2105.05893/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 3 of 3)

- [A deep learning approach to diabetic blood glucose prediction](https://mlsanity.com/paper/1707.05828/) (2017)
- [Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique](https://mlsanity.com/paper/1704.08769/) (2017)
- [A direct approach for function approximation on data defined manifolds](https://mlsanity.com/paper/1908.00156/) (2019)

## Built on by (most cited first, 2 of 2)

- [Local approximation of operators](https://mlsanity.com/paper/2202.06392/) (2022)
- [Learning on manifolds without manifold learning](https://mlsanity.com/paper/2402.12687/) (2024)

## Abstract (arXiv)

The problem of real time prediction of blood glucose (BG) levels based on the readings from a continuous glucose monitoring (CGM) device is a problem of great importance in diabetes care, and therefore, has attracted a lot of research in recent years, especially based on machine learning. An accurate prediction with a 30, 60, or 90 minute prediction horizon has the potential of saving millions of dollars in emergency care costs. In this paper, we treat the problem as one of function approximation, where the value of the BG level at time t+h (where h the prediction horizon) is considered to be an unknown function of d readings prior to the time t. This unknown function may be supported in particular on some unknown submanifold of the d-dimensional Euclidean space. While manifold learning is classically done in a semi-supervised setting, where the entire data has to be known in advance, we use recent ideas to achieve an accurate function approximation in a supervised setting; i.e., construct a model for the target function. We use the state-of-the-art clinically relevant PRED-EGA grid to evaluate our results, and demonstrate that for a real life dataset, our method performs better than a standard deep network, especially in hypoglycemic and hyperglycemic regimes. One noteworthy aspect of this work is that the training data and test data may come from different distributions.